The leakage of proprietary and/or confidential data is a continuing problem for organizations such as corporations, governments and universities. Contemporary ubiquitous remote network access to an organization's computers increases productivity and is convenient, but at the same time creates ever greater challenges for protecting the data from being accessed by unauthorized parties such as competitors or criminals. Leakage of enterprise data can result both from intentional activity by unscrupulous employees, as well as unintentional but negligent actions of employees not following robust security procedures
Organizations lack visibility into the access and flow of sensitive documents and information. Administrators lack tools for tracking data access and usage. Tracking the access and flow of enterprise data and preventing leakage are more difficult than ever. Yet, organizations rightly want to limit the access and use of confidential data according to an enterprise-level information control policy.
Some technologies for tracking access and flow of enterprise data compare strings of text to a database of defined information or types of information. However, these technologies do not extend to circumstances where the information is contained in an image, such as an image of one or more bank checks, credit cards, or driver's licenses.
Conventional optical character recognition (OCR) technologies are not capable of accurately determining the contents of images, including whether an image contains sensitive information, quickly, accurately, and/or efficiently enough to satisfy the demands of a data loss prevention (DLP) system. For instance, some conventional image analysis requires substantial computing resources resulting in unacceptable computing resource cost and latency, especially for enterprise applications. Furthermore, conventional systems are limited in their ability to capture, process, and analyze complicated images.
It would be desirable to address these issues.